Maintaining a Discrete Data Representation that Corresponds to Information Contained in Free-Form Text

ABSTRACT

A system includes a data record (such as an Electronic Medical Record (EMR)) and a user interface for modifying (e.g., storing data in) the data record. The data record includes both free-form text elements and discrete data elements. The user interface includes user interface elements for receiving free-form text data. In response to receiving free-form text data via the free-form text user interface elements, a suggested action is identified, such as a suggested action to take in connection with one of the discrete data elements of the data record. Output is generated representing the suggested action. A user provides input indicating acceptance or rejection of the suggested action. The suggested action may be performed in response to the user input.

BACKGROUND

The clinical documentation systems used in connection with many modern electronic medical records (EMRs) include both user interface elements for entering discrete data into EMRs and user interface elements for entering free-form text into EMRs. Examples of common user interface elements for entering discrete data into EMRs include checkboxes and radio buttons to indicate the presence of absence of problems, complications, conditions of family members, and tests performed during a physical examination; date selection controls to capture the onset or end of certain events (e.g., the date on which pain started, when a family member died, or on which treatment began or ended); and dropdown lists to indicate current and past medications. The most common example of a user interface element for entering free-form text into EMRs is a text field.

Although it might seem that it would be desirable to design clinical documentation systems to include only user interface elements for entering discrete data elements in order to eliminate ambiguity and subjectivity from such data entry, typically it is not possible or desirable to represent all possible inputs using discrete user interface elements, even for narrowly-defined subsets of a clinical document. One reason for this is that discrete user interface elements are not capable of capturing uncommon events, conditionality between multiple observations, uncertainty in observations that cannot accurately be represented by a binary parameter value (e.g., “the patient exhibits signs of X”), and causality. More generally, requiring all EMR data to be entered using discrete UI elements would fail to capture the story of a patient and the thought process of the authoring physician in a way that is both memorable and interpretable by the authoring or other physicians.

As a result, clinical documentation systems typically allow free-form text to be entered into EMRs. For example, it is common for such systems to include a free-form text entry field alongside each logical grouping of discrete user interface elements. For example, a free-form text entry field may be provided for a “History of Present Illness” grouping of discrete user interface elements, another free-form text entry field may be provided for a “Family History” grouping of discrete user interface elements, and yet another free-form text entry field may be provided for a “Physical Examination” grouping of discrete user interface elements.

The resulting graphical user interface includes both user interface elements for receiving discrete data input and user interface elements for receiving free-form text input. As a consequence, the underlying data model into which data received using the graphical user interface is stored is also a hybrid of discrete data and free-form text data.

There are at least two problems with such an EMR system:

-   -   (1) Discrete data entry fields tend to be much less user         friendly than free-form text entry fields. For example, a         typical data entry form for a physical examination may contain         hundreds of checkboxes and other discrete data entry user         interface elements. It can be difficult and time-consuming to         find the right checkboxes to check, in comparison to merely         typing or dictating a free-form text description of the same         information. As a result, physicians or others who enter data         into such an EMR system may either accept reduced productivity         and data fidelity by entering data into the discrete data         fields, or bypass the discrete data fields and just enter         free-form text into the corresponding free-form text fields and         thereby forfeit the benefits of the discrete data fields.     -   (2) It is generally not possible to ensure consistency between         the data contained in a free-form text field and data contained         in the corresponding discrete data fields. For example, consider         the case in which one set of discrete data fields contain fields         to capture palliative care instructions for a patient.         Associated with such discrete data fields may be a free-form         text entry field. If the physician chooses to enter instructions         (e.g., a “Do Not Resuscitate” order) into the free-form text         entry field and leaves the discrete data fields blank, then the         information in the free-form text entry field will not be         reflected in the discrete data fields. If the contents of the         discrete data fields are used to drive a clinical workflow, then         such a workflow will not be triggered if the text entry field         contains data but the discrete data fields are empty.

There is, however, substantial value in the discrete data elements, such as for automating common care workflows and for use in decision support processes. One way to address the problems described above is to abstract information contained in free form-text fields, to reconcile the abstracted information with the last known state of the discrete data elements, and to let the user verify the reconciled data state as part of the free-form text input. One drawback of this approach is that to perform reconciliation, decisions may need to be made regarding the appropriate representation of the free-form text data within the discrete data model. Any errors in this process may need to be resolved by the user using a user interface that essentially mirrors or is identical to the user interface that is used for discrete data entry. As a result, the cost of implementing such a system may be substantial if it is implemented as an add-on to an existing third-party EMR system, due to the need to duplicate essential parts of the discrete data entry user interface for use in the reconciliation review process.

SUMMARY

A system includes a data record (such as an Electronic Medical Record (EMR)) and a user interface for modifying (e.g., storing data in) the data record. The data record includes both free-form text elements and discrete data elements. The user interface includes user interface elements for receiving free-form text data. In response to receiving free-form text data via the free-form text user interface elements, a suggested action is identified, such as a suggested action to take in connection with one of the discrete data elements of the data record. Output is generated representing the suggested action. A user provides input indicating acceptance or rejection of the suggested action. The suggested action may be performed in response to the user input.

For example, one embodiment of the present invention is directed to a method performed by at least one computer processor, the method comprising: (A) receiving free-form text via a free-form text user interface element of a user interface; (B) identifying a suggested action to perform in connection with a discrete data element of a data record; (C) providing output representing the suggested action; (D) receiving user input indicating whether the user accepts the suggested action.

Other features and advantages of various aspects and embodiments of the present invention will become apparent from the following description and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an electronic medical record (EMR) according to one embodiment of the present invention;

FIG. 2A is an abstract diagram of a user interface for receiving user input for updating the EMR of FIG. 1 according to one embodiment of the present invention;

FIG. 2B is a concrete diagram of a user interface for receiving user input for updating the EMR of FIG. 1 according to one embodiment of the present invention;

FIG. 3 is a dataflow diagram of a system for updating discrete data elements in the EMR of FIG. 1 based on free-form text provided by a user into the user interfaces of FIGS. 2A-2B; and

FIG. 4 is a flowchart of a method performed by the system of FIG. 3 according to one embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention may be used to significantly reduce the integration costs described above. For example, instead of (or in addition to) displaying abstracted concepts identified in free-form text, embodiments of the present invention may use the abstracted concepts to identify logical actions to be performed in the target EMR. Embodiments of the present invention may inform the user of such identified logical actions and, in response to verification from the user, embodiments of the present invention may perform the recommended logical actions automatically.

By way of background, consider the example electronic medical record (EMR) 100 illustrated in FIG. 1. The EMR 100 may include both discrete data elements 102 (e.g., fields) and free-form text elements 106 (e.g., fields). Examples of discrete data elements include elements having a finite permissible range of values, such as 0 and 1 (or any equivalent binary range of values, such as “Yes” and “No”) or any other finite set of symbols (e.g., {0, 1, 2, 3}, {red, green, blue}, or {alive, deceased}), where a computer is capable of unambiguously determining the meaning of any particular such value. In contrast, a free-form text element may contain any text, such as text input by a human in a natural language, whether or not a computer is capable of unambiguously determining the meaning of such text.

Particular discrete data elements in the EMR 100 may be associated with particular free-form text elements in the EMR 100. In the particular example of FIG. 1:

-   -   Discrete data element 104 a is associated with text element 108         a, as illustrated by association 110 a.     -   Discrete data element 104 b is associated with text element 108         b, as illustrated by association 110 b.     -   Discrete data element 104 c is associated with text elements 108         c and 108 d, as illustrated by associations 110 c and 110 d,         respectively.     -   Discrete data elements 104 d and 104 e are associated with text         element 108 e, as illustrated by associations 110 e and 110 f,         respectively.     -   Discrete data element 104 f is not associated with any of the         text elements 106.     -   Text element 108 f is not associated with any of the discrete         data elements 102.

As these examples illustrate, a single one of the discrete data elements 102 in the EMR 100 may be associated with zero, one, or more of the text elements in the EMR 100, and a single one of the text elements in the EMR 100 may be associated with zero, one, or more of the discrete data elements 102 in the EMR 100. The particular associations shown in FIG. 1 are merely examples for purposes of illustration. In practice, a single EMR may include hundreds or more of discrete data elements and text elements, which may have any kind and number of associations with each other.

In general, the associations 110 a-f represent a degree of semantic similarity between the associated elements, and/or an influence of the content of associated elements on each other. For example, the association 110 a between discrete data element 104 a and the text element 108 a may indicate that both elements 104 a and 108 a contain, or are designed to contain, data representing the presence or absence of foot pain in the patient represented by the EMR 100. Further examples of the associations 110 a-f will be provided below.

Some or all of the associations 110 a-f may be expressly represented and stored as data within the EMR 100. Some or all of the associations 110 a-f may, however, not be stored as data within the EMR 100, or anywhere else. Instead, as will become apparent from the description below, embodiments of the present invention may identify some or all of the relationships dynamically and without storing data representing such relationships within the EMR 100. In fact, although the associations 110 a-f are shown as fixed in FIG. 1, this is merely an example and does not constitute a limitation of the present invention. For example, embodiments of the present invention may dynamically identify an association between one of the text elements 106 and one or more of the discrete data elements 102 based, at least in part, on the contents of the text element at a particular time. As the contents of the text element change, embodiments of the present invention may identify different associations between the text element and various ones of the discrete data elements 102. The examples describe below will illustrate this ability of embodiments of the present invention to dynamically associate text elements with discrete data elements in response to the contents of such text elements.

Now consider the abstract user interface 200 shown in FIG. 2A. FIG. 2A merely shows certain abstract features of the user interface 200 for purposes of illustration. In particular, the user interface 200 includes both discrete data user interface (UI) elements 202 and free-form text UI elements 206. Examples of the discrete data UI elements 202 include checkboxes, radio buttons, dropdown lists, and menus. Examples of free-form text UI elements 206 include single-line text fields, multi-line text fields, including fields having advanced text editing features such as the ability to apply formatting (e.g., font sizes, bold, italics, underline, superscript, subscript, etc.) to text.

Although the user interface 200 may include any number of discrete data UI elements 202 and any number of free-form text UI elements 206, for purposes of example the user interface 200 is shown in FIG. 2A as containing four discrete data UI elements 204 a-d and three free-form text UI elements 208 a-c. In practice, a user interface 200 for entering data into an EMR may include hundreds of discrete data and free-form text UI elements, arranged in a complex layout, in which the discrete data and free-form text UI elements may span multiple pages or tabs, such that not all of such UI elements may be visible to the user at once.

Discrete data UI elements 202 in the user interface 200 may be mapped to (associated with) zero, one, or more corresponding discrete data elements 102 in the EMR 100 of FIG. 1. Similarly, free-form text UI elements 206 in the user interface 200 may be mapped to (associated with) zero, one, or more corresponding free-form text elements 106 in the EMR 100 of FIG. 1. Although not shown in FIG. 1 or 2A, data representing such mappings may be stored. Such mappings may be one-to-one, one-to-many, or many-to-one. When a user provides input to one of the UI elements 204 a-d or 208 a-c in the user interface 200 of FIG. 2A, such input may be copied into, or otherwise used to generate values which are stored into, the associated element(s) in the EMR 100 of FIG. 1. For example, if free-form text UI element 208 a in the user interface 200 of FIG. 2A is associated with free-form text element 108 a in the EMR 100 of FIG. 1, and the user types “John Smith” into the free-form text UI element 208 a, the text “John Smith” may be stored in the free-form text element 108 a. As another example, if discrete data UI element 204 a is a checkbox which is associated with discrete data element 104 a in the EMR 100 of FIG. 1, and the user marks the checkbox as checked, then a value of “checked,” “yes,” “1,” or a similar value may be stored in the discrete data element 104 a.

In practice, user interface elements 208 a-c and 204 a-d in the user interface 200 may be organized hierarchically to reflect the structure of a clinical document, such as by grouping various ones of the text UI elements 208 a-c and the discrete data UI elements 204 a-d into a “Cardiovascular” category, which may itself be one of many groups of UI elements within a “Review of Systems” category. Such a hierarchical grouping establishes a structure indicating which discrete data elements could be associated with particular free-form text elements, and indicating which discrete data elements cannot be associated with particular free-form text elements because they fall within unrelated clinical categories. Content entered into a free-form text UI element within a particular category may be associated with a discrete data UI element within the same category. Certain information entered into a free-form text UI element may, however, not be associated with any discrete data UI element.

Just as the elements 102 and 106 in the EMR 100 of FIG. 1 may have associations 110 a-f with each other, elements 202 and 206 in the user interface 200 of FIG. 2A may have associations with each other. Such associations are not shown in FIG. 2A for ease of illustration. Associations between discrete data UI elements 202 and free-form text UI elements 206 in the user interface 200 of FIG. 2A may be implied from the associations 110 a-f between the discrete data elements 102 and free-form text elements 106 of the EMR 100 of FIG. 1. For example, assume that:

-   -   discrete data UI element 204 a in the user interface 200 of FIG.         2A is mapped to the discrete data element 104 a in the EMR 100         of FIG. 1;     -   free-form text UI element 208 a in the user interface 200 of         FIG. 2A is mapped to the free-form text element 108 a in the EMR         100 of FIG. 1; and     -   discrete data element 104 a in the EMR 100 of FIG. 1 is         associated with free-form text element 108 a.

In this case, an association between discrete data UI element 204 a and free-form text UI element 208 a may be implied from the facts above.

Some or all of the free-form text UI elements 206 in the user interface 200 of FIG. 2A may not be associated (explicitly or implicitly) with any of the discrete data UI elements 202. For example, free-form text UI element 208 a may not be associated with any of the discrete data UI elements 204 a-d. As will be described in more detail below, however, embodiments of the present invention may be used to enable text that is entered into the free-form text UI element 208 a of FIG. 2A to be used to update the values of one or more of the discrete data elements 102 in the EMR 100 of FIG. 1, whether or not the free-form text UI element 208 a is associated with any of the discrete data UI elements 202 in the user interface of FIG. 2A. In fact, the user interface 200 need not include any of the discrete data UI elements 202. In particular, the user interface 200 may not include any discrete data elements that are mapped to elements 102 or 106 in the EMR 100.

For example, referring to FIG. 2B, an example is shown of a particular user interface 250 that may be used in connection with embodiments of the present invention. The user interface 250 includes only a single UI element that is mapped to an element of the EMR 100, namely a single free-form text entry field 252 that is mapped to one of the free-form text elements 106 in the EMR 100. (Assume for purposes of example that field 252 in the user interface of FIG. 2B is mapped to free-form text element 108 a in the EMR 100 of FIG. 1.) Note that the user interface 250 of FIG. 2B does not include any discrete data UI elements that are mapped to elements 102 or 106 of the EMR 100. As a result, and as will be described in more detail below, embodiments of the present invention use the free-form text that is entered into free-form text entry field 252 to update discrete data elements 102 in the EMR 100 indirectly, by generating suggested actions for updating the discrete data elements 102 based on the contents of the free-form text entry field 252 together with the current state of data elements 102, and performing the suggested actions only if, and in response to, user input confirming that such actions should be performed.

Certain particular embodiments of the present invention will now be described. Referring to FIG. 3, a dataflow diagram is shown of a system 300 for performing actions on discrete data elements of the EMR 100 according to one embodiment of the present invention. Referring to FIG. 4, a flowchart is shown of a method 400 performed by the system 300 of FIG. 3 according to one embodiment of the present invention.

The system 300 includes an electronic medical record (EMR) database 302, which includes a plurality of patient data records 101 a-n (where n may be any number). Each of the patient data records 101 a-n may include free-form text elements having associations with discrete data elements, in the manner described above with respect to the patient data record 101 a of FIG. 1. The system 300 also includes an EMR management module 304 which may perform any operation on the EMR database 302, such as creating new patient data records, deleting existing patient data records, and updating the contents of existing patient data records. Because an EMR may include one or more patient data records, any reference herein to operations performed on patient data records should be understood to constitute operations that are performed on EMRS. Although the EMR management module 304 is shown as a single component in FIG. 3 for ease of illustration, in practice the EMR management module 304 may be implemented in other ways, such as an add-on or plug-in to an existing EMR system, or as a component that communicates with an existing EMR system to perform the functions disclosed herein.

The system 300 also includes a user 306 and a computing device 308. The computing device 308 receives input from and provides output to the computing device 308 using any suitable I/O device(s). The computing device 308 may be any kind of computing device, such as a desktop computer, laptop computer, tablet computer, or smartphone. The computing device 308 may provide output to and receive input from the EMR management module 304. The user 306, computing device 308, and EMR management module 304 may communicate with each other via a direct connection or via any type of network, such as the public Internet or a private intranet.

The computing device 308 may display, to the user 306, a user interface containing at least one free-form text UI element, or otherwise prompt or enable the user 306 to provide free-form text input (FIG. 4, operation 402). For example, the computing device 308 may display to the user 306 a user interface of the kind shown in FIG. 2B, in which the free-form text field 252 enables the user to provide free-form text input, such as by speaking and/or typing such input. As another example, the computing device 308 may provide a voice prompt, such as “Describe the patient encounter,” in response to which the user 306 may provide free-form voice input.

Regardless of the manner in which the computing device 308 enables the user 306 to provide free-form text input, the user 306 may provide such free-form text input 310 to the computing device 308, which receives the input 310 (FIG. 4, operation 404). In the example of FIG. 2B, the user 306 has provided the input 310 by speaking (e.g., into a handheld voice recorder, smartphone, or other device having a microphone or other audio capture component), in response to which the system 300 has transcribed the user's speech (using one or both of an automatic speech recognition (ASR) engine and a human transcriptionist). The computing device has then displayed the resulting transcript within the free-form text field 252.

The EMR management module 304 may generate and/or receive the free-form text input 310. For example, the computing device 308 may provide the free-form text input 310 to the EMR management module 304. As another example, the computing device 308 may receive the free-form text input 310, such as in the form of an audio signal representing speech of the user 306, and transcribe the input 310 to produce a transcript of the input 310, and then provide the transcript to the EMR management module 304. As another example, the computing device 308 may provide the free-form text input 310 in the form of an audio signal representing speech of the user 306 to the EMR management module 304, which may then transcribe the input 310 to produce a transcript of the input 310. In these and other cases, the EMR management module obtains text representing the free-form text input 310 of the user 306, such as the text shown in the free-form text input field 252 of the user interface 250 of FIG. 2B.

If the free-form text input 310 is in the form of an audio signal, the EMR management module 304 may receive the audio signal, a transcript thereof, or both. Any reference herein to the free-form text input 310 should be understood to refer to an audio signal, text and/or other data representing such an audio signal (such as a transcript of the audio signal), or a combination thereof.

The EMR management module 304 may identify a patient data record associated with the free-form text input 310 in the EMR database 302 (FIG. 4, operation 406) and update the identified patient data record based on the free-form text input 310 (FIG. 4, operation 408). The EMR management module 304 may identify the patient data record associated with the free-form text input 310 in any of a variety of ways. For example, the user 306 may (e.g., before providing the free-form text input 310), provide other input (not shown) which identifies the patient who is the subject of the free-form text input 310. Such a patient identifier may be used to identify a corresponding patient data record in the EMR database 302. Assume for purposes of example that the identified patient data record is patient data record 101 a.

The EMR management module 304 may update the identified patient data record 101 a based on the free-form text input 310 in any of a variety of ways. For example, the EMR management module 304 may identify a free-form text field corresponding to the free-form text input 310 in the identified patient data record 101 a, and store the free-form text input 310 in the identified field or otherwise update the identified field based on the free-form text input 310.

The EMR management module 304 includes a suggestion module 312, which identifies one or more discrete data elements in the identified patient data record 101 a based on the free-form text input 310 (FIG. 4, operation 410), and which identifies one or more suggested actions to take in connection with the identified discrete data element(s) (FIG. 4, operation 412). Although the suggestion module 312 may identify more than one discrete data element in operation 410 and more than one suggested action in operation 412, the following description will refer to “the identified discrete data element” and “the suggested action” in the singular merely for ease of explanation. Any such references should be understood to refer to one or more identified discrete data elements and one or more suggested actions, respectively.

The suggestion module 312 may identify the discrete data element in operation 410 in any of a variety of ways. For example, as mentioned above, the free-form text UI element (e.g., free-form text field 252) into which the user 306 inputs the free-form text input 310 may be associated with a discrete data element. The suggestion module 312 may identify the discrete data element associated with the free-form text input 310 in operation 410 by identifying the discrete data element that is associated with the free-form text UI element into which the user 306 inputs the free-form text input 310. Alternatively, and as will be described in more detail below, the suggestion module 312 may identify the discrete data element associated with the free-form text input 310 in operation 410 dynamically, i.e., without relying on any predetermined association between free-form text elements and discrete data elements.

The suggestion module 312 may identify any of a variety of suggested actions to take in connection with the identified discrete data element. For example, the suggested action may include storing a specified value in the identified discrete data element, removing the current value from the identified discrete data element, replacing the current value of the identified discrete data element with a specified value, and performing a specified operation on the current value of the identified discrete data element to produce a new value of the identified discrete data element.

The suggestion module 312 may identify the suggested action 314 in any of a variety of ways. For example, the suggestion module 312 may periodically submit some or all of the text in the text entry field 252 to a natural language understanding (NLU) service that abstracts information from the text in the text entry field 252 into one or more concepts. The term “concept,” as used herein, may include, for example, one or more characteristics of the patient represented by the patient data record 101 a, such as whether the patient represented by the patient data record 101 a has or has had a particular medical condition, is taking or has taken a particular medication, or has been provided with particular medical treatment. For generally, the term “concept” may include any fact related to the patient represented by the patient data record 101 a.

The suggestion module 312 may use the resulting abstracted concepts to determine the suggested action(s) 314 to take in connection with the target patient data record 101 a to cause the target patient data record 101 a to reflect the abstracted concepts. For example, the suggestion module 312 may determine whether one or more of the resulting abstracted concepts is inconsistent with one or more of the discrete data elements in the patient data record 101 a. For example, if one of the abstracted concepts indicates that the patient represented by the patient data record 101 a does not have a psychological disorder and one of the discrete data elements in the patient data record 101 a indicates that the patient represented by the patient data record 101 a does have a psychological disorder, the suggestion module 312 may conclude that the abstracted concept is inconsistent with the discrete data element. If and in response to determining that one or more of the resulting abstracted concepts is inconsistent with one or more of the discrete data elements in the patient data record 101 a, the suggestion module 312 may suggest an action that will cause the one or more discrete data elements to become consistent with the one or more abstracted concepts, such as modifying one or more values of one or more discrete data elements to produce one or more modified values of the one or more discrete data elements. For example, the suggestion module 312 may suggest changing the state of the “psychological disorder” discrete data element from “true” to “false.”

As described above, the suggestion module 312 may identify the suggested action 314 based at least in part on the free-form text input 310 received from the user 306. The suggestion module 312 may identify the suggested action 314 based on data in addition to the free-form text input 310, such as data contained in the discrete data elements in the patient data record 101 a and in the EMR database 302 more generally. For example, if the free-form text input 310 states, “discontinue all beta blockers,” and the patient data record 101 a includes a discrete data element representing the medication of “Atenolol,” then the suggestion module 312 may identify “Discontinue Atenolol” as the suggested action 314. Generating such a suggested action 314 requires the suggestion module to understand that Atenolol is a type of beta blocker and to combine/reconcile the discrete data already contained within the patient data record 101 a with the received free-form text input 310.

The suggestion module 312 may provide output 314 representing the suggested action to the user 306 (FIG. 4, operation 414). The output 314 may be received by the computing device 308 and manifested to the user 306 in any of a variety of ways, such as by generating visual and/or auditory output representing the suggested action. FIG. 2B shows various examples of the output 314, in the form of suggested action output 254. In this example, the suggested action output 254 indicates that the suggestion module 312 suggests:

-   -   storing an indication in the patient data record 101 a that the         patient represented by the patient data record 101 a is negative         for the ailment of “Psychological Disorder,” such as by storing         a value of “false” in a “Psychological Disorder” discrete data         element in the patient data record 101 a;     -   storing an indication in the patient data record 101 a that the         father of the patient represented by the patient data record 101         a is deceased, such as by storing a value of “deceased” in a         discrete data element in the patient data record 101 a which         indicates whether the patient's father is alive or deceased;     -   storing an indication in the patient data record 101 a that the         father of the patient represented by the patient data record 101         a has diabetes, such as by storing a value of “true” in a         discrete data element in the patient data record 101 a which         indicates whether the patient's father has diabetes;     -   storing an indication in the patient data record 101 a that the         father of the patient represented by the patient data record 101         a has hypertension, such as by storing a value of “true” in a         discrete data element in the patient data record 101 a which         indicates whether the patient's father has hypertension;     -   storing an indication in the patient data record 101 a that the         mother of the patient represented by the patient data record 101         a has heart disease, such as by storing a value of “true” in a         discrete data element in the patient data record 101 a which         indicates whether the patient's mother has heart disease;     -   storing an indication in the patient data record 101 a that the         mother of the patient represented by the patient data record 101         a has hypertension, such as by storing a value of “true” in a         discrete data element in the patient data record 101 a which         indicates whether the patient's mother has hypertension;     -   storing an indication in the patient data record 101 a that the         mother of the patient represented by the patient data record 101         a is alive, such as by storing a value of “alive” in a discrete         data element in the patient data record 101 a which indicates         whether the patient's mother is alive or deceased; and     -   storing an indication in the patient data record 101 a that a         sibling of the patient represented by the patient data record         101 a has cancer, such as by storing a value of “cancer” in a         current conditions list of the patient's sibling in the patient         data record 101 a.

It can be seen from FIG. 2B that the suggestion module 312 generated the suggested action of changing the state of the “Psychological Disorder” discrete data element in the patient data record 101 a to “negative” based on the following text in the text area 252: “The patient's family history is negative for any mental illnesses.” It can be seen that the other actions suggested in the region 254 were generated similarly based on text in the text area 252.

The output 314 representing the action suggested by the suggestion module 312 may take other forms in addition to or instead of the form shown in FIG. 2B. For example, although the user interface 250 of FIG. 2B does not contain any discrete data UI elements, such elements may be included within the user interface 250. For example, a checkbox labeled “Psychological Disorder” may be added to the user interface 250. The system 300 may provide output 314 representing a suggestion to mark the patient as negative for a psychological disorder by providing output in connection with the checkbox, such as one or more of the following:

-   -   changing the state of the checkbox to reflect the outcome of         performing the suggested action, such as changing the state of         the checkbox to unchecked, or leaving it as unchecked if it is         already unchecked, so that the visual state of the checkbox         corresponds to the outcome of performing the suggested action         314;     -   visually emphasizing the checkbox, such as by changing its         color, displaying a circle or other shape around it, or causing         it to blink; and     -   displaying an instruction in connection with (e.g., near) the         checkbox to modify a state of the checkbox, such as an         instruction stating, “Leave this checkbox unchecked to indicate         that the patient is negative for a psychological disorder” or         “Uncheck this checkbox to indicate that the patient is negative         for a psychological disorder.”

The user 306 may provide response input 316 to the computing device 308 (FIG. 4, operation 416). The response input 316 may indicate whether the user 306 accepts or rejects the suggested action 314. (If the suggested action 314 includes more than one suggested action, then the response input 316 may include separate response input for each such suggested action, or a single input indicating whether the user 306 accepts or rejects all such actions.) The computing device 308 may provide the response input 316 to the EMR management module 304.

The response input 316 may take any of a variety of forms. For example, referring to FIG. 2B, the user interface 250 includes an “Apply Actions” button 256. The user 306 may provide the response input 316 by clicking on the “Apply Actions” button 256 to indicate that the user 306 wishes for the system 300 to perform the suggested actions 314 (i.e., the actions described in area 254). If the user 306 does not wish for the system 300 to perform the suggested actions 314, then the user 306 may not click on the “Apply Actions” button 256.

As another example, if the user interface 250 includes discrete data UI elements, the user 306 may provide the response input 316 by providing input to such elements and/or by declining to provide input to such elements. For example, if the user interface 250 includes a “Psychological Disorder” checkbox, and the system 300 has marked that checkbox as unchecked, then the user 306 may provide response input 316 indicating acceptance of the suggested action 314 by leaving the state of the checkbox as unchecked, or provide response input 316 indicating rejection of the suggested action by changing the state of the checkbox to checked. Conversely, the user 306 may indicate acceptance of the suggested action 314 by changing the state of a discrete data UI element (e.g., by changing the state of a “Father Deceased” checkbox from unchecked to checked), and indicate rejection of the suggested action by leaving the state of the discrete data UI element unchanged.

The suggestion module 312 determines whether the response input 316 indicates that the user 306 accepts the suggested action 314 (FIG. 4, operation 418). If the suggestion module 312 determines that the response input 316 indicates that the user 306 accepts the suggested action 314, then the suggestion module 312 performs the suggested action 314 (FIG. 4, operation 420). Performing the suggested action 314 may include changing the state(s) of one or more discrete data elements in the patient data record 101 a, in the manner suggested by the suggested action 314. For example, performing the suggested actions specified in the region 254 of FIG. 2B may include changing the state of a “Psychological Disorder” discrete data element in the patient data record 101 a to “negative.”

The EMR management module 304 may generate updated output 318 reflecting the state of the patient data record 101 a that resulted from performing the suggested action 314 (FIG. 4, operation 424). The computing device 308 may display or otherwise manifest the updated output 318 to the user 306, such as by changing the state of a discrete data UI element in the user interface 250 to reflect the state of an associated discrete data element in the patient data record 101 a that was updated by the performance of the suggested action 314. For example, in response to changing the state of the “Psychological Disorder” discrete data element in the patient data record 101 a to “negative,” the EMR management module 304 may generate updated output 318 containing data representing the negative state of the “Psychological Disorder” discrete data element. The computing device 308 may render such output by, for example, changing the state of a “Psychological Disorder” checkbox in the user interface 250 to be unchecked. In this way, the system 300 informs the user 306 of the effects of performing the suggested action 314.

If the suggestion module 312 determines that the response input 316 indicates that the user 306 does not accept the suggested action 314, then the suggestion module 312 does not perform the suggested action 314 (FIG. 4, operation 422).

In the examples provided above, the suggestion module 312 provides at most one suggested action in connection with any particular discrete data element in the patient data record 101 a. This is merely an example, however, and does not constitute a limitation of the present invention. The suggestion module 312 may, for example, generate multiple suggested actions in connection with a single discrete data element in the patient data record 101 a, including multiple suggested actions which have inconsistent outcomes (e.g., a suggestion to change the state of a discrete data element to “true” and a suggestion to change the state of the discrete data element to “false”). For example, consider a situation in which a free-form text UI element contains the text, “Nodules in left lower lobe suggestive of cancer, but there are no clear indications that would make such a diagnosis possible at this time. Recommend follow up biopsy.” The suggestion module may, based on such free-form text, identify any one or more of the following actions: “Add breast cancer to problem list,” “Remove breast cancer from problem list,” and “Create order for biopsy of lung tissue.” In this example, the information contained in the free-form text UI element was ambiguous. In response to such ambiguity, the suggestion module 312 generated two alternative and mutually-exclusive suggested actions (“Add breast cancer to problem list” and “Remove breast cancer from problem list”), thereby making it easier for the user 306 (e.g., physician) to accept one of these two suggested actions even though the system 300 did not determined which of the two suggested actions is represented by the free-form text. Furthermore, in this example the system 300 has generated an additional suggested action (“Create order for biopsy of lung tissue”) that is not part of the clinical report itself, but that is a logical next step, given the contents of the free-form text UI element.

As the preceding example further illustrates, embodiments of the present invention are not limited to making suggestions to perform actions on user interface elements. Embodiments of the present invention may make suggestions to directly modify the contents of an EMR (e.g., “Add breast cancer to problem list”). As another example, embodiments of the present invention may make suggestions to perform actions other than modifying the contents of an EMR, such as suggestions to trigger a workflow (e.g., “Create order for biopsy of lung tissue”).

Consider another example in which a free-form text UI element contains the text, “Patient recently started to take Celexa against his depressions.” The suggestion module 312 may, based on such free-form text, identify the following suggested action: “Add Celexa to the list of current medications.” Such a suggested action may not be sufficiently defined to enable the action to be performed fully automatically. For example, the suggested action may lack necessary information, such as start date and dosage. The generation of such a suggested action may, however, be supplemented by automatically displaying the medication list of the patient data record 101 a to the user 306 (e.g., physician) to facilitate the process of the user 306 adding Celexa to the medication list. As another example, the EMR management module 304 may add Celexa to the medication list in the patient data record 101 a automatically and enter any additional available data automatically, and then enable the user 306 to enter any remaining necessary data manually. Such a process may make it easier for the user 306 to add a medication to the medication list even though the process does not perform all steps of adding the medication to the medication list automatically.

Embodiments of the present invention have a variety of advantages. For example, as described above, conventional EMR systems often include both free-form text elements and discrete data elements for representing the same information. As a result, it is common for the data in such redundant elements to be inconsistent with each other. For example, a “Psychological Disorder” free-form text field in an EMR may include the text, “Patient suffers from schizophrenia,” while a “Psychological Disorder” discrete data element in the same EMR may include the value “false.” Such inconsistencies can be particularly difficult to detect, prevent, and eliminate in modern EMR systems, particularly because a single EMR may include hundreds of fields, and because the user interface elements representing those fields (e.g., text fields and checkboxes) may be not be displayed to the user in a manner which makes clear that the fields are intended to contain the same information as each other or otherwise be related to each other. For example, the “Psychological Disorder” text field may be displayed far on the screen from the “Psychological Disorder” checkbox. Embodiments of the present invention enable actual and possible inconsistencies between corresponding free-form text elements and discrete data elements to be identified automatically, thereby increasing the likelihood that such inconsistencies will be eliminated. Such elimination assists in maintaining the validity of the discrete data elements the EMR, thereby increasing the utility of such data elements for purposes such as automatic clinical decision support.

A related advantage of embodiments of the present invention is that they may suggest actions to be performed to eliminate inconsistencies between free-form text elements and discrete data elements, but not perform such actions automatically. Instead, embodiments of the present invention may present such suggested actions to a user, such a physician, for approval, and only perform such suggested actions if and in response to approval from the user. Requiring user approval provides protection against errors by the suggestion module 312. Furthermore, in cases in which the suggestion module 312 suggests multiple actions which are inconsistent with each other as a result of failure of the free-form text input 310 to indicate a particular action with sufficient specificity, the user 306 must provide approval before any action may be taken. Although requiring user approval may cause inconsistencies to be eliminated more slowly and with more user effort than would be required by a fully automated system, requiring user approval balances such increases in efficiency with the benefits derived from performing automatically-suggested actions which are not justified by the underlying free-form text. Furthermore, embodiments of the present invention minimize the amount of effort required by the user 306, by enabling the user 306 to authorize the suggested actions 314 to be performed with a minimum of input (such as by clicking on the single “Apply Actions” button 256 in FIG. 2B), and by enabling the suggested actions 314 to be performed automatically in response to approval from the user 306.

Another advantage of embodiments of the present invention is that they may learn from the user 306′s acceptance and rejection of suggested actions 314 to improve the suggestion module 312′s future action suggestions. For example, each time the user 306 provides action response input 316 which accepts or rejects an action suggested by the suggested action output 314, the system 300 may store a record containing data representing one or more of the following:

-   -   the input 316 (e.g., whether the input 316 represents an         acceptance or a rejection);     -   the suggested action 314 associated with the input 316;     -   any data that was used by the suggestion module 312 to generate         the suggested action 314 (such as the free-form text input 310         and any values of discrete data elements in the EMR database 302         that were used to generate the suggested action 314); and     -   the discrete data element(s) suggested to be updated by the         suggested action.

The system 300 may store such data for each of a plurality of suggested actions 314 and corresponding response inputs 316 from the user 306 (and possibly from other users). The system 300 may use any suitable machine learning technique to use such store data to modify the process by which the suggestion module 312 generates suggested actions 314 in the future. In general, if the system 300 determines that the stored data represents statistically significant evidence that a particular action should be suggested in the future in a particular context, then the system 300 may suggest that the particular action be performed in that particular context in the future. Similarly, if the system 300 determines that the stored data represents statistically significant evidence that a particular action should not be suggested in the future in a particular context, then the system 300 may not suggest that the particular action be performed in that particular context in the future. Such a technique enables the system 300 to improve the quality of its suggestions over time. For example, if the system 300 repeatedly suggests performing a particular action in a context in which the user 306 consistently rejects the suggestion, then the system 300 may use the stored data representing the user 306′s consistent rejection of the suggested action to stop suggesting that action in the same context in the future.

It is to be understood that although the invention has been described above in terms of particular embodiments, the foregoing embodiments are provided as illustrative only, and do not limit or define the scope of the invention. Various other embodiments, including but not limited to the following, are also within the scope of the claims. For example, elements and components described herein may be further divided into additional components or joined together to form fewer components for performing the same functions. As a particular example, the suggestion module 312 may be implemented solely in the EMR management module 304, be implemented solely in the computing device 308, be distributed across both the EMR management module 304 and the computing device 308, or in any other suitable manner to perform the functions disclosed herein.

The techniques disclosed herein are not limited to use in connection with any particular EMR system, or in connection with any particular EMR data. The particular EMR data elements disclosed herein are merely examples and do not constitute limitations of the present invention. Other EMR data elements include, but are not limited to, current and historical medication lists, allergies, and current and historical medical problems.

The techniques disclosed herein may be integrated into an EMR system and/or work by communicating with an EMR system. In the latter case, data may, for example, be transferred from the EMR system to a system implementing the techniques disclosed herein using an ASTM Continuity of Care Record (CCR) or an HL7 CDA Continuity of Care Document (CCD).

The techniques described above may be implemented, for example, in hardware, software tangibly embodied in a computer-readable medium, firmware, or any combination thereof. The techniques described above may be implemented in one or more computer programs executing on a programmable computer including a processor, a storage medium readable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code may be applied to input entered using the input device to perform the functions described and to generate output. The output may be provided to one or more output devices.

Each computer program within the scope of the claims below may be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language may, for example, be a compiled or interpreted programming language.

Each such computer program may be implemented in a computer program product tangibly embodied in non-transitory signals in a machine-readable storage device for execution by a computer processor. Method steps of the invention may be performed by a computer processor executing a program tangibly embodied on a computer-readable medium to perform functions of the invention by operating on input and generating output. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, the processor receives instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions include, for example, all forms of non-volatile memory, such as semiconductor memory devices, including EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs. Any of the foregoing may be supplemented by, or incorporated in, specially-designed ASICs (application-specific integrated circuits) or FPGAs (Field-Programmable Gate Arrays). A computer can generally also receive programs and data from a storage medium such as an internal disk (not shown) or a removable disk. These elements will also be found in a conventional desktop or workstation computer as well as other computers suitable for executing computer programs implementing the methods described herein, which may be used in conjunction with any digital print engine or marking engine, display monitor, or other raster output device capable of producing color or gray scale pixels on paper, film, display screen, or other output medium. 

1. A computer-implemented method performed by at least one computer processor, the method comprising: (A) receiving data in a first data representation; (B) extracting free-form text from the first data representation; (C) identifying, based on the free-form text, a plurality of discrete data elements of a data structure; (D) identifying, based on the free-form text, the plurality of suggested actions, wherein the plurality of suggested actions comprises, for each discrete data element D in the plurality of discrete data elements of a data structure, a corresponding suggested action S comprising: storing a value in the discrete data element D, removing a current value of the discrete data element D, or performing a specified operation on a current value of the discrete data element D to produce a new value of the discrete data element D; and (E) performing the at least one of the plurality of suggested actions.
 2. The method of claim 1, wherein the first data representation comprises at least one of an audio signal representing speech and a transcript of the audio signal representing speech.
 3. The method of claim 2, wherein (B) comprises: (B1) detecting the format of the first data representation; (B2) when an audio signal is detected, converting, using automatic speech recognition, the audio signal into a transcript corresponding to the speech present in the audio signal and parsing the transcript to extract the free-form text; and (B3) when a transcript is detected, parsing the transcript to extract the free-form text.
 4. The method of claim 1, wherein the data structure comprises an electronic medical record.
 5. The method of claim 1, further comprising: (F) after identifying the plurality of suggested actions, providing output representing, for each discrete data element D in the plurality of discrete data elements of the data structure, the corresponding suggested action S.
 6. The method of claim 5, further comprising: (G) receiving a user input indicating user's acceptance of the at least one of the plurality of suggested actions, wherein the at least one of the plurality of suggested actions is performed in response to the receiving the user input.
 7. The method of claim 6, further comprising: (H) storing a record of the at least one of the plurality of suggested actions and the user input; (I) repeating (A)-(G) at least once to produce a plurality of stored records of the plurality of suggested actions and a plurality of corresponding user inputs; and (J) modifying a process used to identify the plurality of suggested actions based on the plurality of stored records.
 8. The method of claim 7, further comprising: (K) displaying output representing outcome of performing the at least one of the plurality of suggested actions.
 9. The method of claim 1, wherein (D) comprises: (D)(1) identifying a concept based on the free-form text; (D)(2) determining, for each discrete data element D in the plurality of discrete data elements of the data structure, whether the concept is consistent with the discrete data element D; and (D)(3) in response to determining that the concept is not consistent with the discrete data element D, identifying a suggested action, wherein the identified suggested action causes the discrete data element D to be consistent with the concept.
 10. The method of claim 1, further comprising: (F) before receiving the free-form text, receiving a patient identifier, and (G) before identifying the plurality of discrete data elements of the data structure, identifying the data structure associated with the patient identifier.
 11. A non-transitory computer-readable medium comprising computer program instructions executable by at least one computer processor to perform a method, the method comprising: (A) receiving data in a first data representation; (B) selecting free-form text from the first data representation; (C) identifying, based on the free-form text, a plurality of discrete data elements of a data structure; (D) identifying, based on the free-form text, the plurality of suggested actions, wherein the plurality of suggested actions comprises, for each discrete data element D in the plurality of discrete data elements of a data structure, a corresponding suggested action S comprising: storing a value in the discrete data element D, removing a current value of the discrete data element D, or performing a specified operation on a current value of the discrete data element D to produce a new value of the discrete data element D; and (E) performing the at least one of the plurality of suggested actions.
 12. The non-transitory computer-readable medium of claim 11, wherein the first data representation comprises at least one of an audio signal representing speech and a transcript of the audio signal representing speech.
 13. The non-transitory computer-readable medium of claim 12, wherein (B) comprises: (B1) detecting the format of the first data representation; (B2) when an audio signal is detected, converting, using automatic speech recognition, the audio signal into a transcript corresponding to the speech present in the audio signal and parsing the transcript to select the free-form text; and (B3) when a transcript is detected, parsing the transcript to select the free-form text.
 14. The non-transitory computer-readable medium of claim 11, wherein the data structure comprises an electronic medical record.
 15. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: (F) after identifying the plurality of suggested actions, providing output representing, for each discrete data element D in the plurality of discrete data elements of the data structure, the corresponding suggested action S.
 16. The non-transitory computer-readable medium of claim 15, where the method further comprises: (G) receiving a user input indicating user's acceptance of the at least one of the plurality of suggested actions, wherein the at least one of the plurality of suggested actions is performed in response to the receiving the user input.
 17. The non-transitory computer-readable medium of claim 16, where the method further comprises: (H) storing a record of the at least one of the plurality of suggested actions and the user input; (I) repeating (A)-(G) at least once to produce a plurality of stored records of the plurality of suggested actions and a plurality of corresponding user inputs; and (J) modifying a process used to identify the plurality of suggested actions based on the plurality of stored records.
 18. The non-transitory computer-readable medium of claim 17, where the method further comprises: (K) displaying output representing outcome of performing the at least one of the plurality of suggested actions.
 19. The non-transitory computer-readable medium of claim 1, wherein (D) comprises: (D)(1) identifying a concept based on the free-form text; (D)(2) determining, for each discrete data element D in the plurality of discrete data elements of the data structure, whether the concept is consistent with the discrete data element D; and (D)(3) in response to determining that the concept is not consistent with the discrete data element D, identifying a suggested action, wherein the identified suggested action causes the discrete data element D to be consistent with the concept.
 20. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: (F) before receiving the free-form text, receiving a patient identifier, and (G) before identifying the plurality of discrete data elements of the data structure, identifying the data structure associated with the patient identifier. 